Highly Efficient Machine Learning Approach for Automatic Disease and Color Classification of Olive Fruits-Reference-Cited by-同舟云学术

Highly Efficient Machine Learning Approach for Automatic Disease and Color Classification of Olive Fruits

Author:

Hassan Nashaat M. Hussain¹^ORCID,Donkol A. A.²^ORCID,Mabrook M. Mourad³^ORCID,Mabrouk A. M.⁴

Affiliation:

1. Faculty of Engineering and Technology, Badr University in Cairo (BUC), Badr City, Egypt

2. Communication and Electronics Department, Faculty of Engineering, Electrical Engineering, South Valley University, Qena, Egypt

3. Space Communication Department, Faculty of Navigation Science and Space Technology, Beni-Suef University, Beni Suef, Egypt

4. Faculty of Engineering, King Salman International University (KSIU), South Sinai, El Tor, Egypt

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

Reference37 articles.

1. Machine learning for olive fruit diseases classification system;El-aziz

2. Classification of olive disease based on non-linear deep features;Ayaz;Appl. Sci. J., MDPI, Appl. Sci.,2021

5. Effective workflow for high-performance recognition of fruits using machine learning approaches;Kader;Int. Res. J. Eng. Technol. (IRJET),2020

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